subject:( en SWARM INTELLIGENCE)

Western Carolina University

1. Proffitt, Matthew R. Optimization of swarm robotic constellation communication for object detection and event recognition.

Degree: 2011, Western Carolina University

URL: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874

► Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are… (more)

▼ Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are far beyond the capabilities of a single agent. This self organizing but decentralized form of intelligence requires that all members are autonomous and act upon their available information. From this information they are able to decide their behavior and take the appropriate action. A global behavior can then be witnessed that is derived from the local behaviors of each agent. The presented research introduces the novel method for optimizing the communication and the processing of communicated data for the purpose of detecting large scale meta object or event, denoted as meta event, which are unquantifiable through a single robotic agent. The ability of a swarm of robotic agents to cover a relatively large physical environment and their ability to detect changes or anomalies within the environment is especially advantageous for the detection of objects and the recognition of events such as oil spills, hurricanes, and large scale security monitoring. In contrast a single robot, even with much greater capabilities, could not explore or cover multiple areas of the same environment simultaneously. Many previous swarm behaviors have been developed focusing on the rules governing the local agent to agent behaviors of separation, alignment, and cohesion. By effectively optimizing these simple behaviors in coordination, through cooperative and competitive actions based on a chosen local behavior, it is possible to achieve an optimized global emergent behavior of locating a meta object or event. From the local to global relationship an optimized control algorithm was developed following the basic rules of swarm behavior for the purpose of meta event detection and recognition. Results of this optimized control algorithm are presented and compared with other work in the field of swarm robotics.; Communication, Coordination, Detection, Optimization, Robotics, Swarm Advisors/Committee Members: Brian Howell (advisor).

Subjects/Keywords: Robotics; Swarm intelligence

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Proffitt, M. R. (2011). Optimization of swarm robotic constellation communication for object detection and event recognition. (Masters Thesis). Western Carolina University. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874

Chicago Manual of Style (16^th Edition):

Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Masters Thesis, Western Carolina University. Accessed January 20, 2021. http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.

MLA Handbook (7^th Edition):

Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Web. 20 Jan 2021.

Vancouver:

Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Internet] [Masters thesis]. Western Carolina University; 2011. [cited 2021 Jan 20]. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.

Council of Science Editors:

Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Masters Thesis]. Western Carolina University; 2011. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874

2. NC DOCKS at Western Carolina University; Proffitt, Matthew R. Optimization of swarm robotic constellation communication for object detection and event recognition.

Degree: 2011, NC Docks

URL: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf

► Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are… (more)

Subjects/Keywords: Robotics; Swarm intelligence

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

NC DOCKS at Western Carolina University; Proffitt, M. R. (2011). Optimization of swarm robotic constellation communication for object detection and event recognition. (Thesis). NC Docks. Retrieved from http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

NC DOCKS at Western Carolina University; Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Thesis, NC Docks. Accessed January 20, 2021. http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

NC DOCKS at Western Carolina University; Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Web. 20 Jan 2021.

Vancouver:

NC DOCKS at Western Carolina University; Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Internet] [Thesis]. NC Docks; 2011. [cited 2021 Jan 20]. Available from: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

NC DOCKS at Western Carolina University; Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Thesis]. NC Docks; 2011. Available from: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Baylor University

3. Yu, Albert Reynold, 1984-. Optimizing multi-agent dynamics for underwater tactical applications.

Degree: M.S.E.C.E., Engineering., 2011, Baylor University

URL: http://hdl.handle.net/2104/8181

► Large groups of autonomous agents, or swarms, can exhibit complex emergent behaviors that are difficult to predict and characterize from their low-level interactions. These emergent… (more)

Subjects/Keywords: Swarm intelligence.; Multi-agent systems.; Swarm inversion.

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yu, Albert Reynold, 1. (2011). Optimizing multi-agent dynamics for underwater tactical applications. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/8181

Chicago Manual of Style (16^th Edition):

Yu, Albert Reynold, 1984-. “Optimizing multi-agent dynamics for underwater tactical applications.” 2011. Masters Thesis, Baylor University. Accessed January 20, 2021. http://hdl.handle.net/2104/8181.

MLA Handbook (7^th Edition):

Yu, Albert Reynold, 1984-. “Optimizing multi-agent dynamics for underwater tactical applications.” 2011. Web. 20 Jan 2021.

Vancouver:

Yu, Albert Reynold 1. Optimizing multi-agent dynamics for underwater tactical applications. [Internet] [Masters thesis]. Baylor University; 2011. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2104/8181.

Council of Science Editors:

Yu, Albert Reynold 1. Optimizing multi-agent dynamics for underwater tactical applications. [Masters Thesis]. Baylor University; 2011. Available from: http://hdl.handle.net/2104/8181

Pontifical Catholic University of Rio de Janeiro

4. MANOELA RABELLO KOHLER. [en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION.

Degree: 2019, Pontifical Catholic University of Rio de Janeiro

URL: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=43545

► [pt] O algoritmo de otimização por enxame de partículas (PSO, do inglês Particle Swarm Optimization) é uma meta-heurística baseada em populações de indivíduos na qual… (more)

▼ [pt] O algoritmo de otimização por enxame de partículas (PSO, do inglês Particle Swarm Optimization) é uma meta-heurística baseada em populações de indivíduos na qual os candidatos à solução evoluem através da simulação de um modelo simplificado de adaptação social. Juntando robustez, eficiência e simplicidade, o PSO tem adquirido grande popularidade. São reportadas muitas aplicações bem-sucedidas do PSO nas quais este algoritmo demonstrou ter vantagens sobre outras meta-heurísticas bem estabelecidas baseadas em populações de indivíduos. Algoritmos modificados de PSO já foram propostos para resolver problemas de otimização com restrições de domínio, lineares e não lineares. A grande maioria desses algoritmos utilizam métodos de penalização, que possuem, em geral, inúmeras limitações, como por exemplo: (i) cuidado adicional ao se determinar a penalidade apropriada para cada problema, pois deve-se manter o equilíbrio entre a obtenção de soluções válidas e a busca pelo ótimo; (ii) supõem que todas as soluções devem ser avaliadas. Outros algoritmos que utilizam otimização multi-objetivo para tratar problemas restritos enfrentam o problema de não haver garantia de se encontrar soluções válidas. Os algoritmos PSO propostos até hoje que lidam com restrições, de forma a garantir soluções válidas utilizando operadores de viabilidade de soluções e de forma a não necessitar de avaliação de soluções inválidas, ou somente tratam restrições de domínio controlando a velocidade de deslocamento de partículas no enxame, ou o fazem de forma ineficiente, reinicializando aleatoriamente cada partícula inválida do enxame, o que pode tornar inviável a otimização de determinados problemas. Este trabalho apresenta um novo algoritmo de otimização por enxame de partículas, denominado PSO+, capaz de resolver problemas com restrições lineares e não lineares de forma a solucionar essas deficiências. A modelagem do algoritmo agrega seis diferentes capacidades para resolver problemas de otimização com restrições: (i) redirecionamento aritmético de validade de partículas; (ii) dois enxames de partículas, onde cada enxame tem um papel específico na otimização do problema; (iii) um novo método de atualização de partículas para inserir diversidade no enxame e melhorar a cobertura do espaço de busca, permitindo que a borda do espaço de busca válido seja devidamente explorada – o que é especialmente conveniente quando o problema a ser otimizado envolve restrições ativas no ótimo ou próximas do ótimo; (iv) duas heurísticas de criação da população inicial do enxame com o objetivo de acelerar a inicialização das partículas, facilitar a geração da população inicial válida e garantir diversidade no ponto de partida do processo de otimização; (v) topologia de vizinhança, denominada vizinhança de agrupamento aleatório coordenado para minimizar o problema de convergência prematura da otimização; (vi) módulo de transformação de restrições de igualdade em restrições de desigualdade. O algoritmo foi testado em vinte e quatro funções benchmarks – criadas e propostas… Advisors/Committee Members: RICARDO TANSCHEIT.

Subjects/Keywords: [pt] OTIMIZACAO; [en] OPTIMIZATION; [pt] RESTRICOES LINEARES; [en] LINEAR RESTRICTIONS; [pt] PARTICLE SWARM OPTIMIZATION; [pt] ENXAME DE PARTICULAS; [en] PARTICLE SWARM; [pt] INTELIGENCIA DE ENXAMES; [en] SWARM INTELLIGENCE; [pt] RESTRICAO NAO LINEAR; [en] NONLINEAR RESTRICTION

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

KOHLER, M. R. (2019). [en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=43545

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

KOHLER, MANOELA RABELLO. “[en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION.” 2019. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed January 20, 2021. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=43545.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

KOHLER, MANOELA RABELLO. “[en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION.” 2019. Web. 20 Jan 2021.

Vancouver:

KOHLER MR. [en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. [cited 2021 Jan 20]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=43545.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

KOHLER MR. [en] PSO+: A LINEAR AND NONLINEAR CONSTRAINTS-HANDLING PARTICLE SWARM OPTIMIZATION. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=43545

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Delaware

5. Sun, Yu. Modeling and analyzing large swarms with covert leaders.

Degree: PhD, University of Delaware, Department of Mathematical Sciences, 2015, University of Delaware

URL: http://udspace.udel.edu/handle/19716/17195

► Swarm dynamics is the study of collections of agents that interact with one another without central control. We would like to build a model to… (more)

▼ Swarm dynamics is the study of collections of agents that interact with one another without central control. We would like to build a model to simulate the behavior of swarms with covert leaders. Then based on the model, we would like to know the stability of the system, the collective decision of the swarms when there is knowledge confliction, and the way to find the covert leaders when we observe a group of individuals in motion. In Chapter 1, we extend the covert leadership model in large swarms. A leader is a member of the swarm that acts upon information in addition to what is provided by local interactions. A covert leader is a leader that is treated no differently than others in the swarm, so leaders and followers participate equally in whatever interaction model is used. We focus our efforts on the behaviors driven by the three-zone swarming model and present a new nonlinear model in which leaders will respond more strongly to additional information when the swarm is less dense. Similarly, leaders in dense regions behave more like followers. In Chapter 2, we perform linear stability analysis on the model. The result is the same as the leaderless model, which says that the growth or decay of perturbations in an infinite, uniform swarm depends on the strength of attraction relative to repulsion and orientation. It tells us that we could inject additional information into the system without changing the stability criteria. We verify our analysis with simulation. We also compare our model with more popular linear leadership models. The leaders in our model are embedded in the swarms instead of accumulating into the front in contrast to the linear model. We apply this model to wireless robotic applications, in which densities are calculated utilizing positions of neighboring robots. The result on the QualNet platform is consistent with our ideal simulation results. In Chapter 3, we explore problems where two classes of covert leaders with different information try to influence the same swarm. The swarms will choose the average direction if the information differential is small. The swarms will randomly choose a direction of the leaders' if the information differential is large. We validate our modeling and analysis using realistic wireless protocols and channel models on the QualNet network simulator. We also perform two case studies which are simplified forms of our model to find the bifurcation point analytically. In Chapter 4, we try to solve the problem: whether or not it is possible to distinguish between followers and leaders when we observe a group of individuals in motion. We explore the interplay between swarm dynamics, covert leadership and theoretical information transfer. Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions. We use theoretical information transfer as a means of analyzing swarm interactions. We find that covert leaders can be distinguished from followers in a swarm because they receive less transfer… Advisors/Committee Members: Rossi, Louis F..

Subjects/Keywords: Swarm intelligence – Mathematical models.

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sun, Y. (2015). Modeling and analyzing large swarms with covert leaders. (Doctoral Dissertation). University of Delaware. Retrieved from http://udspace.udel.edu/handle/19716/17195

Chicago Manual of Style (16^th Edition):

Sun, Yu. “Modeling and analyzing large swarms with covert leaders.” 2015. Doctoral Dissertation, University of Delaware. Accessed January 20, 2021. http://udspace.udel.edu/handle/19716/17195.

MLA Handbook (7^th Edition):

Sun, Yu. “Modeling and analyzing large swarms with covert leaders.” 2015. Web. 20 Jan 2021.

Vancouver:

Sun Y. Modeling and analyzing large swarms with covert leaders. [Internet] [Doctoral dissertation]. University of Delaware; 2015. [cited 2021 Jan 20]. Available from: http://udspace.udel.edu/handle/19716/17195.

Council of Science Editors:

Sun Y. Modeling and analyzing large swarms with covert leaders. [Doctoral Dissertation]. University of Delaware; 2015. Available from: http://udspace.udel.edu/handle/19716/17195

University of Windsor

6. Zhang, Miao. Digital Filter Design Using Improved Teaching-Learning-Based Optimization.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Windsor

URL: https://scholar.uwindsor.ca/etd/7856

► Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse… (more)

▼ Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness. Advisors/Committee Members: Kwan, H.K..

Subjects/Keywords: digital filters; swarm intelligence algorithms

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, M. (2019). Digital Filter Design Using Improved Teaching-Learning-Based Optimization. (Doctoral Dissertation). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7856

Chicago Manual of Style (16^th Edition):

Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Doctoral Dissertation, University of Windsor. Accessed January 20, 2021. https://scholar.uwindsor.ca/etd/7856.

MLA Handbook (7^th Edition):

Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Web. 20 Jan 2021.

Vancouver:

Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Internet] [Doctoral dissertation]. University of Windsor; 2019. [cited 2021 Jan 20]. Available from: https://scholar.uwindsor.ca/etd/7856.

Council of Science Editors:

Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Doctoral Dissertation]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7856

Vanderbilt University

7. Kirkpatrick, Douglas Andrew. Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.

Degree: MS, Computer Science, 2015, Vanderbilt University

URL: http://hdl.handle.net/1803/11788

► The capabilities of robots have increased to the point where it is more effective and economical to field a large group of less expensive robots… (more)

Subjects/Keywords: biological swarms; swarm; robotics; swarm intelligence; swarm robotics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kirkpatrick, D. A. (2015). Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11788

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kirkpatrick, Douglas Andrew. “Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.” 2015. Thesis, Vanderbilt University. Accessed January 20, 2021. http://hdl.handle.net/1803/11788.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kirkpatrick, Douglas Andrew. “Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.” 2015. Web. 20 Jan 2021.

Vancouver:

Kirkpatrick DA. Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. [Internet] [Thesis]. Vanderbilt University; 2015. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1803/11788.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kirkpatrick DA. Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. [Thesis]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/11788

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University

8. Cody, Jason Robert. Discrete Consensus Decisions in Human-Collective Teams.

Degree: PhD, Computer Science, 2018, Vanderbilt University

URL: http://hdl.handle.net/1803/11985

► Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions… (more)

▼ Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions that are facilitated by information pooling within a shared decision space, similar to an insect colony. Biological collectives must frequently choose the best option from a finite set of options and execute an action based on that choice. Discrete collective consensus achievement algorithms have enabled robotic collectives to make decisions, but most research does not consider scenarios in which the collective must ignore biasing features within the environment. Whether the collective decides between occupation sites, routes, or future actions, the environmental features (e.g., distance between a resource and the collective) alter robot interactions and bias collective decisions towards options that are the easiest to find, evaluate, and reach, but may not be the optimal choice. Robotic collectives that must ignore biasing environmental features during decision making are likely to be inaccurate and inefficient. Robotic collectives do not have centralized control; thus, they are challenged to synchronize the initiation and execution of the chosen actions, which is critical to future collectives that must respond to the environment and complete complex tasks. Discrete collective consensus achievement strategies have only recently been considered in the field of Human-Swarm Interaction, which has largely focused on enabling humans to control artificial swarms. Typically, swarms are comprised of agents that interact according to a protocol that causes a desired emergent behavior, such as flocking. Most Human-Swarm Interaction research assumes the human has near-perfect knowledge of the swarm and global communication with the swarm's agents. Robotic collectives have the potential to share decision making functions with humans; however, methods of human interaction with collective discrete consensus strategies have not been designed or evaluated. This dissertation develops a new algorithm influenced by biologically inspired discrete consensus achievement strategies in order to enable robotic collectives to choose and implement the best actions, despite the presence of environmental bias. The new model enables future human-collective teams to make decisions when the human does not have perfect global knowledge. Further, human-collective interaction mechanisms are developed in order to facilitate collaborative decisions between a human and a simulated robotic collective. The robotic collective model is implemented and evaluated for its ability to act independently and as a part of a human-collective team in trials featuring the human supervision of multiple targeting collectives. Advisors/Committee Members: Yevgeniy Vorobeychik (committee member), Maithilee Kunda (committee member), Jennifer S. Trueblood (committee member), Alexander S. Mentis (committee member), Julie A. Adams (Committee Chair).

Subjects/Keywords: multi-agent systems; swarm intelligence; collective decision making; human-swarm interaction

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cody, J. R. (2018). Discrete Consensus Decisions in Human-Collective Teams. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11985

Chicago Manual of Style (16^th Edition):

Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed January 20, 2021. http://hdl.handle.net/1803/11985.

MLA Handbook (7^th Edition):

Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Web. 20 Jan 2021.

Vancouver:

Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1803/11985.

Council of Science Editors:

Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://hdl.handle.net/1803/11985

NSYSU

9. PRATHYUSHA, YERRA. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

► The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs… (more)

Subjects/Keywords: Swarm intelligence; UAV; Particle Swarm Optimization (PSO); Navigation algorithm; Path planning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

PRATHYUSHA, Y. (2018). UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Web. 20 Jan 2021.

Vancouver:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

10. Talukder, Satyobroto. Mathematicle Modelling and Applications of Particle Swarm Optimization.

Degree: 2011, , School of Engineering

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

► Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or… (more)

Subjects/Keywords: Optimization; swarm intelligence; particle swarm; social network; convergence; stagnation; multi-objective.

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Talukder, S. (2011). Mathematicle Modelling and Applications of Particle Swarm Optimization. (Thesis). , School of Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Thesis, , School of Engineering. Accessed January 20, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Web. 20 Jan 2021.

Vancouver:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2021 Jan 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Thesis]. , School of Engineering; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Sudheer Menon, Vishnu. Decentralized Approach to SLAM using Computationally Limited Robots.

Degree: MS, 2017, Worcester Polytechnic Institute

URL: etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315

► Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave… (more)

Subjects/Keywords: Multi-robot systems; SLAM; Swarm intelligence; Swarm robotics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sudheer Menon, V. (2017). Decentralized Approach to SLAM using Computationally Limited Robots. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sudheer Menon, Vishnu. “Decentralized Approach to SLAM using Computationally Limited Robots.” 2017. Thesis, Worcester Polytechnic Institute. Accessed January 20, 2021. etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sudheer Menon, Vishnu. “Decentralized Approach to SLAM using Computationally Limited Robots.” 2017. Web. 20 Jan 2021.

Vancouver:

Sudheer Menon V. Decentralized Approach to SLAM using Computationally Limited Robots. [Internet] [Thesis]. Worcester Polytechnic Institute; 2017. [cited 2021 Jan 20]. Available from: etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sudheer Menon V. Decentralized Approach to SLAM using Computationally Limited Robots. [Thesis]. Worcester Polytechnic Institute; 2017. Available from: etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

12. Potanapalli, Kranti Kumar. Coactive learning for multi-robot search and coverage.

Degree: MS, Computer Science, 2013, Oregon State University

URL: http://hdl.handle.net/1957/45107

► We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed… (more)

Subjects/Keywords: Coactive Learning; Swarm intelligence – Mathematical models

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Potanapalli, K. K. (2013). Coactive learning for multi-robot search and coverage. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/45107

Chicago Manual of Style (16^th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Masters Thesis, Oregon State University. Accessed January 20, 2021. http://hdl.handle.net/1957/45107.

MLA Handbook (7^th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Web. 20 Jan 2021.

Vancouver:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1957/45107.

Council of Science Editors:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/45107

Univerzitet u Beogradu

13. Bačanin-Džakula, Nebojša V. , 1983-. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.

Degree: Matematički fakultet, 2016, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get

►

Računarstvo - Veštačka inteligencija / Computer Science - Artificial Intelligence

Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim metodama, uspe²no se poslednjih… (more)

▼

Računarstvo - Veštačka inteligencija / Computer Science - Artificial Intelligence

Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim metodama, uspe²no se poslednjih godina re²avaju populacionim stohasti£kim metaheuristikama, meu kojima istaknutu klasu predstavljaju algoritmi inteligencije rojeva. U ovom radu razmatra se unapreenje metaheuristika inteligencije rojeva pomo¢u hibridizacije. Analizom postoje¢ih metaheuristika u odreenim slu£ajevima uo£eni su nedostaci i slabosti u mehanizmima pretrage prostora re²enja koji pre svega proisti£u iz samog matemati£kog modela kojim se simulira proces iz prirode kao i iz nedovoljno usklaenog balansa izmeu intenzikacije i diversikacije. U radu je ispitivano da li se postoje¢i algoritmi inteligencije rojeva za globalnu optimizaciju mogu unaprediti (u smislu dobijanja boljih rezultata, brºe konvergencije, ve¢e robustnosti) hibridizacijom sa drugim algoritmima. Razvijeno je i implementirano vi²e hibridizovanih metaheuristika inteligencije rojeva. S obzirom da dobri hibridi ne nastaju slu£ajnom kombinacijom pojedinih funkcionalnih elemenata i procedura razli£itih algoritama, ve¢ su oni utemeljeni na sveobuhvatnom izu£avanju na£ina na koji algoritmi koji se hibridizuju funkcioni²u, kreiranju hibridnih pristupa prethodila je detaljna analiza prednosti i nedostataka posmatranih algoritma kako bi se napravila najbolja kombinacija koja nedostatke jednih neutrali²e prednostima drugih pristupa. Razvijeni hibridni algoritmi verikovani su testiranjima na standardnim skupovima test funkcija za globalnu optimizaciju sa ograni£enjima i bez ograni£enja, kao i na poznatim prakti£nim problemima. Uporeivanjem sa najboljim poznatim algoritmima iz literature pokazan je kvalitet razvijenih hibrida, £ime je potvrena i osnovna hipoteza ovog rada da se algoritmi inteligencije rojeva mogu uspe²no unaprediti hibridizacijom.

Advisors/Committee Members: Tuba, Milan, 1952-.

Subjects/Keywords: swarm intelligence metaheuristics; hybrid algorithms; global optimization

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bačanin-Džakula, Nebojša V. , 1. (2016). Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.” 2016. Thesis, Univerzitet u Beogradu. Accessed January 20, 2021. https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.” 2016. Web. 20 Jan 2021.

Vancouver:

Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2021 Jan 20]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

14. Geiger, Peter C. A comparison of novel stochastic optimization methods.

Degree: 2015, University of Georgia

URL: http://hdl.handle.net/10724/32903

► Many real world problems are too complex to solve with traditional programming methods in a reasonable amount of time. Stochastic optimization techniques have been applied… (more)

Subjects/Keywords: Optimization; Genetic Algorithm; Swarm Intelligence; Repair Operators

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Geiger, P. C. (2015). A comparison of novel stochastic optimization methods. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/32903

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Geiger, Peter C. “A comparison of novel stochastic optimization methods.” 2015. Thesis, University of Georgia. Accessed January 20, 2021. http://hdl.handle.net/10724/32903.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Geiger, Peter C. “A comparison of novel stochastic optimization methods.” 2015. Web. 20 Jan 2021.

Vancouver:

Geiger PC. A comparison of novel stochastic optimization methods. [Internet] [Thesis]. University of Georgia; 2015. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10724/32903.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Geiger PC. A comparison of novel stochastic optimization methods. [Thesis]. University of Georgia; 2015. Available from: http://hdl.handle.net/10724/32903

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

The Ohio State University

15. Gazi, Veysel. Stability analysis of swarms.

Degree: PhD, Graduate School, 2002, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Subjects/Keywords: Engineering; Swarm intelligence

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Gazi, V. (2002). Stability analysis of swarms. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Chicago Manual of Style (16^th Edition):

Gazi, Veysel. “Stability analysis of swarms.” 2002. Doctoral Dissertation, The Ohio State University. Accessed January 20, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.

MLA Handbook (7^th Edition):

Gazi, Veysel. “Stability analysis of swarms.” 2002. Web. 20 Jan 2021.

Vancouver:

Gazi V. Stability analysis of swarms. [Internet] [Doctoral dissertation]. The Ohio State University; 2002. [cited 2021 Jan 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.

Council of Science Editors:

Gazi V. Stability analysis of swarms. [Doctoral Dissertation]. The Ohio State University; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Vanderbilt University

16. Sengupta, Saptarshi. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.

Degree: PhD, Electrical Engineering, 2019, Vanderbilt University

URL: http://hdl.handle.net/1803/14438

► With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part… (more)

▼ With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part from the limitations of specific optimization methods implied by the No Free Lunch Theorem. Applications of computational intelligence are growing exponentially with the widespread availability of increasingly powerful computers. This has made feasible the mimicry of highly interactive multi-agent models of natural systems that solve complicated problems while remaining stable. The emergent behaviors arising in such systems hint at novel methods of optimization that can find solutions to machine learning problems of similar complexity. This dissertation introduces a social, agent-based (swarm) intelligence algorithm viz. the Quantum Double Delta Swarm (QDDS). It is modeled after the mechanism of convergence, under an attractive potential field, to the center of a single well in a double Dirac delta potential-well problem. The swarming model developed here extends the well-known Quantum-behaved Particle Swarm Optimization (QPSO) algorithm to the more stable, double well configuration for optimal solutions of complex engineering design problems. Theoretical foundations and experimental illustrations lead to applications of the model to find solutions of problems in intrinsically high-dimensional feature spaces. In addition, the effects of chaos on the exploratory capacity of the algorithm are studied by including a Chebyshev map driven exploration (C-QDDS) step and benchmarking the results. Visualization of the process is enabled by tracking the trajectory of the best performing agent in each iteration over all episodes across benchmark contours. Under general assumptions common to random search convergence proofs the dynamical limitations of this model’s convergence are critically analyzed. Finally, results are demonstrated on: a) the multidimensional finite impulse response (FIR) filter design problem and b) Neuro-evolution, specifically using a two-layer neural architecture where the C-QDDS search mutates candidate architectures whose weights and biases are then trained using gradient-free swarming. Advisors/Committee Members: Douglas Hardin (committee member), Nilanjan Sarkar (committee member), Don Mitchell Wilkes (committee member), Kazuhiko Kawamura (committee member), Alan Peters (Committee Chair).

Subjects/Keywords: quantum-inspired computational intelligence; stochastic optimization; particle swarm optimization; multiagent systems; swarm intelligence; distributed artificial intelligence

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sengupta, S. (2019). QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14438

Chicago Manual of Style (16^th Edition):

Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed January 20, 2021. http://hdl.handle.net/1803/14438.

MLA Handbook (7^th Edition):

Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Web. 20 Jan 2021.

Vancouver:

Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1803/14438.

Council of Science Editors:

Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/14438

Brno University of Technology

17. Winklerová, Zdenka. Inteligence skupiny: Swarm Intelligence.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/63276

► The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the… (more)

Subjects/Keywords: Kolektivní inteligence; inteligence skupiny; rojová inteligence; optimalizace rojem částic; Collective intelligence; group intelligence; swarm intelligence; particle swarm optimization.

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Winklerová, Z. (2019). Inteligence skupiny: Swarm Intelligence. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/63276

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Winklerová, Zdenka. “Inteligence skupiny: Swarm Intelligence.” 2019. Thesis, Brno University of Technology. Accessed January 20, 2021. http://hdl.handle.net/11012/63276.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Winklerová, Zdenka. “Inteligence skupiny: Swarm Intelligence.” 2019. Web. 20 Jan 2021.

Vancouver:

Winklerová Z. Inteligence skupiny: Swarm Intelligence. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/11012/63276.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Winklerová Z. Inteligence skupiny: Swarm Intelligence. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/63276

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Kelman, Alexander. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.

Degree: Informatics, 2017, University of Skövde

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

► The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess… (more)

Subjects/Keywords: Swarm Intelligence; Pathfinding; Ant Colony Optimization; Particle Swarm Optimization; A*; Computer Sciences; Datavetenskap (datalogi)

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kelman, A. (2017). Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Thesis, University of Skövde. Accessed January 20, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Web. 20 Jan 2021.

Vancouver:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2021 Jan 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Thesis]. University of Skövde; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

19. Magg, Sven. Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.

Degree: PhD, 2012, University of Hertfordshire

URL: http://hdl.handle.net/2299/9038

► The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in… (more)

▼ The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that can not be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. In order to keep individual robots simple, a control algorithm has to keep expensive communication to a minimum and has to be able to act on little information to keep the amount of sensors down. The number of sensors and actuators can be reduced even more when necessary capabilities are spread out over different agents that then combine them by cooperating. Self-organised differentiation within these heterogeneous groups has to take the individual abilities of agents into account to improve group performance. In this thesis it is shown that a homogeneous group of versatile agents can not be easily replaced by a heterogeneous group, by separating the abilities of the versatile agents into several specialists. It is shown that no composition of those specialists produces the same outcome as a homogeneous group on a clustering task. In the second part of this work, an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is analysed in more detail. It does not require communication and needs only the information on individual success or failure. The algorithm leads to self-organised regulation of group activity depending on object availability in the environment by adjusting resting times in a base. A possible variation of this algorithm is introduced which replaces the probabilistic mechanism with which agents determine to leave the base. It is demonstrated that a direct calculation of the resting times does not lead to differences in terms of differentiation and speed of adaptation. After investigating effects of different parameters on the system, it is shown that there is no efficiency increase in static environments with constant object density when using a homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic environments. The algorithm was also reported to lead to higher activity of agents which have higher performance. It is shown that this leads to efficiency gains in heterogeneous groups in static and dynamic environments.

Subjects/Keywords: 629.892; self-organisation; differentiation; talk allocation; adaptive behaviour; swarm intelligence; multi-agent-systems; swarm robotics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Magg, S. (2012). Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. (Doctoral Dissertation). University of Hertfordshire. Retrieved from http://hdl.handle.net/2299/9038

Chicago Manual of Style (16^th Edition):

Magg, Sven. “Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.” 2012. Doctoral Dissertation, University of Hertfordshire. Accessed January 20, 2021. http://hdl.handle.net/2299/9038.

MLA Handbook (7^th Edition):

Magg, Sven. “Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.” 2012. Web. 20 Jan 2021.

Vancouver:

Magg S. Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. [Internet] [Doctoral dissertation]. University of Hertfordshire; 2012. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2299/9038.

Council of Science Editors:

Magg S. Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. [Doctoral Dissertation]. University of Hertfordshire; 2012. Available from: http://hdl.handle.net/2299/9038

University of Georgia

20. Oruganti Venkata, Sanjay Sarma. A swarm engineering framework for microtubule self-organization.

Degree: 2018, University of Georgia

URL: http://hdl.handle.net/10724/37465

► Microtubules are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta tubulins combine to form these tubules through polymerization, controlled… (more)

Subjects/Keywords: Microtubules; Microtubule Associated Proteins; Self-Organization; Swarm Engineering; Swarm Intelligence; Game Engine; Protofilaments

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Oruganti Venkata, S. S. (2018). A swarm engineering framework for microtubule self-organization. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37465

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2018. Thesis, University of Georgia. Accessed January 20, 2021. http://hdl.handle.net/10724/37465.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2018. Web. 20 Jan 2021.

Vancouver:

Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10724/37465.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37465

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New Mexico

21. Lu, Qi. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.

Degree: Department of Computer Science, 2019, University of New Mexico

URL: https://digitalrepository.unm.edu/cs_etds/100

► Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area… (more)

▼ Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine… Advisors/Committee Members: Melanie E. Moses, Carlo Pinciroli, Stephanie Forrest, Joshua P. Hecker.

Subjects/Keywords: Swarm Robotics; Swarm Intelligence; Bio-Inspired Robot Swarm; Autonomous Robot; Foraging Robots; Multi-agent Systems; Robotics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lu, Q. (2019). An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. (Doctoral Dissertation). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/cs_etds/100

Chicago Manual of Style (16^th Edition):

Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Doctoral Dissertation, University of New Mexico. Accessed January 20, 2021. https://digitalrepository.unm.edu/cs_etds/100.

MLA Handbook (7^th Edition):

Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Web. 20 Jan 2021.

Vancouver:

Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Internet] [Doctoral dissertation]. University of New Mexico; 2019. [cited 2021 Jan 20]. Available from: https://digitalrepository.unm.edu/cs_etds/100.

Council of Science Editors:

Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Doctoral Dissertation]. University of New Mexico; 2019. Available from: https://digitalrepository.unm.edu/cs_etds/100

Brno University of Technology

22. Veselý, Filip. Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.

Degree: 2016, Brno University of Technology

URL: http://hdl.handle.net/11012/53237

► This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work… (more)

Subjects/Keywords: umělá inteligence; inteligence roje; částicové hejno; optimalizace; PSO; artificial intelligence; swarm intelligence; particle swarm; optimization; PSO

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Veselý, F. (2016). Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53237

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Veselý, Filip. “Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.” 2016. Thesis, Brno University of Technology. Accessed January 20, 2021. http://hdl.handle.net/11012/53237.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Veselý, Filip. “Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.” 2016. Web. 20 Jan 2021.

Vancouver:

Veselý F. Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. [Internet] [Thesis]. Brno University of Technology; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/11012/53237.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Veselý F. Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. [Thesis]. Brno University of Technology; 2016. Available from: http://hdl.handle.net/11012/53237

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Pretoria

23. [No author]. Particle swarm optimisation in dynamically changing environments - an empirical study .

Degree: 2012, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).… (more)

Subjects/Keywords: Atomic pso; Charged pso; Self-adapting multi-swarm; Re-evaluating pso; Particle swarm optimisation (pso); Dynamically changing environment; Quantum swarm optimisation; Reinitialising pso; Computational intelligence; Multi-swarm; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

author], [. (2012). Particle swarm optimisation in dynamically changing environments - an empirical study . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

Chicago Manual of Style (16^th Edition):

author], [No. “Particle swarm optimisation in dynamically changing environments - an empirical study .” 2012. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.

MLA Handbook (7^th Edition):

author], [No. “Particle swarm optimisation in dynamically changing environments - an empirical study .” 2012. Web. 20 Jan 2021.

Vancouver:

author] [. Particle swarm optimisation in dynamically changing environments - an empirical study . [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 20]. Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.

Council of Science Editors:

author] [. Particle swarm optimisation in dynamically changing environments - an empirical study . [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

University of Pretoria

24. Duhain, Julien Georges Omer Louis. Particle swarm optimisation in dynamically changing environments - an empirical study.

Degree: Computer Science, 2012, University of Pretoria

URL: http://hdl.handle.net/2263/25875

► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).… (more)

Subjects/Keywords: Atomic PSO; Charged PSO; Self-adapting multi-swarm; Re-evaluating PSO; Particle swarm optimisation (PSO); Dynamically changing environment; Quantum swarm optimisation; Reinitialising PSO; Computational intelligence; Multi-swarm; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Duhain, J. G. O. (2012). Particle swarm optimisation in dynamically changing environments - an empirical study. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/25875

Chicago Manual of Style (16^th Edition):

Duhain, Julien Georges Omer. “Particle swarm optimisation in dynamically changing environments - an empirical study.” 2012. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://hdl.handle.net/2263/25875.

MLA Handbook (7^th Edition):

Duhain, Julien Georges Omer. “Particle swarm optimisation in dynamically changing environments - an empirical study.” 2012. Web. 20 Jan 2021.

Vancouver:

Duhain JGO. Particle swarm optimisation in dynamically changing environments - an empirical study. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2263/25875.

Council of Science Editors:

Duhain JGO. Particle swarm optimisation in dynamically changing environments - an empirical study. [Masters Thesis]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/25875

25. Sun, Yanxia. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.

Degree: Docteur es, Information Scientifique et Technique, 2011, Université Paris-Est

URL: http://www.theses.fr/2011PEST1049

►

Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des… (more)

▼

Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des points se déplaçant dans un espace multidimensionnel. Le poids aléatoire des paramètres de contrôle est utilisé pour provoquer les particules à aller stochastiquement vers une région ayant plus de succès dans un espace tridimensionnel. Les particules itératives ajustent leur vitesse et leur direction en fonction de leurs personnels et des meilleures positions dans l'essaim. PSO a été appliquée avec succès pour optimiser une large gamme de problèmes. Cependant, les algorithmes standard PSO sont facilement piégés dans les points locaux suboptimaux lorsqu'il est appliqué à des problèmes avec de nombreux extrema locaux ou avec des contraintes. Cette thèse présente plusieurs algorithmes / techniques pour améliorer la capacité de l'OPS recherche mondiale: 1) Deux nouveaux algorithmes chaotiques de particules essaim d'optimisation, d'avoir une chaotiques Hopfield Neural Network (HNN) la structure, sont proposées. L'utilisation d'un système chaotique pour déterminer les poids des particules aide des algorithmes OSP pour échapper à des extrema locaux et de trouver l'optimum global. 2) Pour les algorithmes existants OSP, la relation et l'influence compter que sur les composants correspondants dimensions de l'essaim de particules. Pour montrer la relation intérieure entre les différentes composantes d'une particule, les réseaux de neurones peuvent être utilisés pour modéliser les projections d'ordre du problème d'optimisation, et une optimisation des intérieurs entièrement connecté essaim de particules est proposé à cet effet. 3) En raison de la complexité des contraintes, une solution déterministe générale est souvent difficile à trouver. Par conséquent, une particule détendue contrainte optimisation par essaim algorithme est proposé. Cette méthode améliore la capacité de recherche de l'OSP. 4) Pour améliorer les performances de l'optimisation par essaim de particules, une méthode adaptative de particules essaim d'optimisation basée sur les tests d'hypothèses sont proposées. Cette méthode applique un test d'hypothèse pour déterminer si le piège des particules dans un minimum local ou non. 5) Afin de renforcer la capacité du MPSO de recherche globale, une approche adaptative multi-objectif l'optimisation par essaim de particules (MOPSO) est proposé. Les résultats de simulation et d'analyse confirment l'efficacité des algorithmes proposés / techniques par rapport à l'autre état d'algorithmes

Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called “particles” conceptualized as moving points in a multidimensional space. The random weights of the control parameters are used to cause the particles to stochastically move towards a successful region in a higher dimensional space. Particles iteratively…

Advisors/Committee Members: Siarry, Patrick (thesis director).

Subjects/Keywords: Optimisation par essaim de particules; Le chaos; Intelligence; Particle swarm optimization; Chaos; Intelligence

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sun, Y. (2011). Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2011PEST1049

Chicago Manual of Style (16^th Edition):

Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Doctoral Dissertation, Université Paris-Est. Accessed January 20, 2021. http://www.theses.fr/2011PEST1049.

MLA Handbook (7^th Edition):

Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Web. 20 Jan 2021.

Vancouver:

Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Internet] [Doctoral dissertation]. Université Paris-Est; 2011. [cited 2021 Jan 20]. Available from: http://www.theses.fr/2011PEST1049.

Council of Science Editors:

Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Doctoral Dissertation]. Université Paris-Est; 2011. Available from: http://www.theses.fr/2011PEST1049

26. Steyven, Andreas Siegfried Wilhelm. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.

Degree: PhD, 2017, Edinburgh Napier University

URL: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

► This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called… (more)

Subjects/Keywords: 006.3; Computer science; swarm robotics; artificial intelligence; 006.3 Artificial intelligence; QA75 Electronic computers. Computer science

10 more images…

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Steyven, A. S. W. (2017). A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. (Doctoral Dissertation). Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

Chicago Manual of Style (16^th Edition):

Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Doctoral Dissertation, Edinburgh Napier University. Accessed January 20, 2021. http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.

MLA Handbook (7^th Edition):

Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Web. 20 Jan 2021.

Vancouver:

Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Internet] [Doctoral dissertation]. Edinburgh Napier University; 2017. [cited 2021 Jan 20]. Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.

Council of Science Editors:

Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Doctoral Dissertation]. Edinburgh Napier University; 2017. Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

University of Pretoria

27. [No author]. Using particle swarm optimization to evolve two-player game agents .

Degree: 2007, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

► Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents… (more)

Subjects/Keywords: Swarm intelligence; Computer games; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

author], [. (2007). Using particle swarm optimization to evolve two-player game agents . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

Chicago Manual of Style (16^th Edition):

author], [No. “Using particle swarm optimization to evolve two-player game agents .” 2007. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.

MLA Handbook (7^th Edition):

author], [No. “Using particle swarm optimization to evolve two-player game agents .” 2007. Web. 20 Jan 2021.

Vancouver:

author] [. Using particle swarm optimization to evolve two-player game agents . [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2021 Jan 20]. Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.

Council of Science Editors:

author] [. Using particle swarm optimization to evolve two-player game agents . [Masters Thesis]. University of Pretoria; 2007. Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

University of Pretoria

28. Rakitianskaia, A.S. (Anastassia Sergeevna). Using particle swarm optimisation to train feedforward neural networks in dynamic environments .

Degree: 2012, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/

► The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied… (more)

Subjects/Keywords: Computational intelligence; Particle swarm optimization (PSO); Concept drift; Neural networks; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rakitianskaia, A. S. (. (2012). Using particle swarm optimisation to train feedforward neural networks in dynamic environments . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-02132012-233212/

Chicago Manual of Style (16^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments .” 2012. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.

MLA Handbook (7^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments .” 2012. Web. 20 Jan 2021.

Vancouver:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments . [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 20]. Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.

Council of Science Editors:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments . [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/

University of Pretoria

29. [No author]. Using SetPSO to determine RNA secondary structure .

Degree: 2009, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/

► RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule… (more)

Subjects/Keywords: Rna; Secondary structure; Setpso; Combinatorial; Computational intelligence; Particle swarm optimiser; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

author], [. (2009). Using SetPSO to determine RNA secondary structure . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-02162009-112429/

Chicago Manual of Style (16^th Edition):

author], [No. “Using SetPSO to determine RNA secondary structure .” 2009. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.

MLA Handbook (7^th Edition):

author], [No. “Using SetPSO to determine RNA secondary structure .” 2009. Web. 20 Jan 2021.

Vancouver:

author] [. Using SetPSO to determine RNA secondary structure . [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2021 Jan 20]. Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.

Council of Science Editors:

author] [. Using SetPSO to determine RNA secondary structure . [Masters Thesis]. University of Pretoria; 2009. Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/

University of Pretoria

30. [No author]. A study of gradient based particle swarm optimisers .

Degree: 2011, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/

► Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm… (more)

Subjects/Keywords: Artificial intelligence; Gradient methods; Hybrid; Particle swarm optimisers; UCTD

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

author], [. (2011). A study of gradient based particle swarm optimisers . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-11292010-143123/

Chicago Manual of Style (16^th Edition):

author], [No. “A study of gradient based particle swarm optimisers .” 2011. Masters Thesis, University of Pretoria. Accessed January 20, 2021. http://upetd.up.ac.za/thesis/available/etd-11292010-143123/.

MLA Handbook (7^th Edition):

author], [No. “A study of gradient based particle swarm optimisers .” 2011. Web. 20 Jan 2021.

Vancouver:

author] [. A study of gradient based particle swarm optimisers . [Internet] [Masters thesis]. University of Pretoria; 2011. [cited 2021 Jan 20]. Available from: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/.

Council of Science Editors:

author] [. A study of gradient based particle swarm optimisers . [Masters Thesis]. University of Pretoria; 2011. Available from: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/

Open Access Theses and Dissertations